Background:
The significant number of protein-protein interactions (PPIs) discovered
by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry
and two-hybrid screening suggests astonishing prospects for remodelling drug discovery.
The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential
therapeutic targets for every human disease. In order to allow modern drug discovery
programs to leverage this, we should be able to discern complete PPI maps associated with a
specific disorder and corresponding normal physiology.
Objective:
Here, we will review community available computational programs for predicting
PPIs and web-based resources for storing experimentally annotated interactions.
Methods:
We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS,
COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.
Results:
We described sequence-based and structure-based PPI prediction tools and addressed
their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends
on the quality and quantity of the experimental data they are built on; we extensively
discussed community resources for protein interactions. We focused on the active and recently
updated primary and secondary PPI databases, repositories specialized to the subject or
species, as well as databases that include both experimental and predicted PPIs.
Conclusion:
PPI complexes are the basis of important physiological processes and therefore,
possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed
up new target discoveries through prioritization of therapeutically relevant protein–protein
complexes for experimental studies.
A Web-Based Framework for the Automated Data Analysis and Visualization of Lipid-Protein Interactions